Novel treatments in autism spectrum disorder
Bibliographic record
Abstract
PURPOSE OF REVIEW: There are currently no approved medications for the core symptoms of autism spectrum disorder (ASD), and only limited data on the management of co-occurring mental health and behavioural symptoms. The purpose of this review is to synthesize recent trials on novel treatments in ASD, with a focus on research trends in the past 2 years. RECENT FINDINGS: No new pharmacologic agents received regulatory approval for use in ASD. Several large randomized controlled trials (RCTs) had negative or ambiguous results (e.g. fluoxetine, oxytocin). A cross-over RCT of an oral cannabinoid suggested possible benefits for disruptive behaviours. Two large-scale multicentre trials of bumetanide were terminated early for lack of efficacy. Multicenter trials using repetitive transcranial magnetic stimulation are underway. Recent meta-analyses indicate that specific behavioural and psychological interventions can support social communication and treat anxiety. Numerous novel treatment targets informed by biological mechanisms are under investigation. SUMMARY: Recent data support the use of behavioural and psychological interventions for social communication and anxiety in ASD; data are more limited regarding pharmacotherapy for core and associated symptoms. Next steps include replication of early findings, trials of new molecular targets, and the identification of novel biomarkers, including genetic predictors, of treatment response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".